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Geomechanics and Engineering
  Volume 1, Number 4, December 2009, pages 307-321
DOI: http://dx.doi.org/10.12989/gae.2009.1.4.307
 


Modeling sulfuric acid induced swell in carbonate clays using artificial neural networks
P.V. Sivapullaiah, B. Guru Prasad and M.M. Allam

 
Abstract
    The paper employs a feed forward neural network with back-propagation algorithm for modeling time dependent swell in clays containing carbonate in the presence of sulfuric acid. The oedometer swell percent is estimated at a nominal surcharge pressure of 6.25 kPa to develop 612 data sets for modeling. The input parameters used in the network include time, sulfuric acid concentration, carbonate percentage, and liquid limit. Among the total data sets, 280 (46%) were assigned to training, 175 (29%) for testing and the remaining 157 data sets (25%) were relegated to cross validation. The network was programmed to process this information and predict the percent swell at any time, knowing the variable involved. The study demonstrates that it is possible to develop a general BPNN model that can predict time dependent swell with relatively high accuracy with observed data (R2=0.9986). The obtained results are also compared with generated non-linear regression model.
 
Key Words
    artificial neural networks; swell percent; calcareous clay; sulfuric acid.
 
Address
P.V. Sivapullaiah: Department of Civil Engineering, Indian Institute of Science, Bangalore - 560 012, India
B. Guru Prasad: Department of Civil Engineering, University of Wollongong, Wollongong-2500, NSW, Australia
M.M. Allam: Department of Civil Engineering, Indian Institute of Science, Bangalore - 560 012, India
 

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